New technique to estimate the asymmetric trimming mean

A. M H Alkhazaleh, Ahmad Mahir Razali

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A trimming mean eliminates the extreme observations by removing observations from each end of the ordered sample. In this paper, we adopted the Hogg's and Brys's tail weight measures. In addition, a new algorithm was proposed as a linear estimator based on the quartile; we used a quartile to divide the data into three and four groups. Then two new estimators were proposed. These classes of linear estimators were examined via simulation method over a variety of asymmetric distributions. Sample sizes 50, 100, 150, and 200 were generated using R program. The results of 50 were tabulated, since we have similar results for the other sizes. These results were tabulated for 7 asymmetric distributions with total trimmed proportions 0.10 and 0.20 on both sides, respectively. The results for these estimators were ordered based on their relative efficiency.

Original languageEnglish
Article number739154
JournalJournal of Probability and Statistics
DOIs
Publication statusPublished - 2010

Fingerprint

Trimming
Quartile
Asymmetric Distribution
Linear Estimator
Estimate
Estimator
Relative Efficiency
Simulation Methods
Divides
Tail
Sample Size
Extremes
Proportion
Eliminate
Observation

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

New technique to estimate the asymmetric trimming mean. / Alkhazaleh, A. M H; Razali, Ahmad Mahir.

In: Journal of Probability and Statistics, 2010.

Research output: Contribution to journalArticle

@article{2d14db21497d42b1ab71556d1cc4b128,
title = "New technique to estimate the asymmetric trimming mean",
abstract = "A trimming mean eliminates the extreme observations by removing observations from each end of the ordered sample. In this paper, we adopted the Hogg's and Brys's tail weight measures. In addition, a new algorithm was proposed as a linear estimator based on the quartile; we used a quartile to divide the data into three and four groups. Then two new estimators were proposed. These classes of linear estimators were examined via simulation method over a variety of asymmetric distributions. Sample sizes 50, 100, 150, and 200 were generated using R program. The results of 50 were tabulated, since we have similar results for the other sizes. These results were tabulated for 7 asymmetric distributions with total trimmed proportions 0.10 and 0.20 on both sides, respectively. The results for these estimators were ordered based on their relative efficiency.",
author = "Alkhazaleh, {A. M H} and Razali, {Ahmad Mahir}",
year = "2010",
doi = "10.1155/2010/739154",
language = "English",
journal = "Journal of Probability and Statistics",
issn = "1687-952X",
publisher = "Hindawi Publishing Corporation",

}

TY - JOUR

T1 - New technique to estimate the asymmetric trimming mean

AU - Alkhazaleh, A. M H

AU - Razali, Ahmad Mahir

PY - 2010

Y1 - 2010

N2 - A trimming mean eliminates the extreme observations by removing observations from each end of the ordered sample. In this paper, we adopted the Hogg's and Brys's tail weight measures. In addition, a new algorithm was proposed as a linear estimator based on the quartile; we used a quartile to divide the data into three and four groups. Then two new estimators were proposed. These classes of linear estimators were examined via simulation method over a variety of asymmetric distributions. Sample sizes 50, 100, 150, and 200 were generated using R program. The results of 50 were tabulated, since we have similar results for the other sizes. These results were tabulated for 7 asymmetric distributions with total trimmed proportions 0.10 and 0.20 on both sides, respectively. The results for these estimators were ordered based on their relative efficiency.

AB - A trimming mean eliminates the extreme observations by removing observations from each end of the ordered sample. In this paper, we adopted the Hogg's and Brys's tail weight measures. In addition, a new algorithm was proposed as a linear estimator based on the quartile; we used a quartile to divide the data into three and four groups. Then two new estimators were proposed. These classes of linear estimators were examined via simulation method over a variety of asymmetric distributions. Sample sizes 50, 100, 150, and 200 were generated using R program. The results of 50 were tabulated, since we have similar results for the other sizes. These results were tabulated for 7 asymmetric distributions with total trimmed proportions 0.10 and 0.20 on both sides, respectively. The results for these estimators were ordered based on their relative efficiency.

UR - http://www.scopus.com/inward/record.url?scp=84859181530&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84859181530&partnerID=8YFLogxK

U2 - 10.1155/2010/739154

DO - 10.1155/2010/739154

M3 - Article

AN - SCOPUS:84859181530

JO - Journal of Probability and Statistics

JF - Journal of Probability and Statistics

SN - 1687-952X

M1 - 739154

ER -